Technologies for acquiring medical images via various modalities have been used. A medical image for diagnosis is configured such that a brightness value based on a location inside the image varies depending on the reactivity of a material constituting an organ of a human body, and is acquired by reconstructing a set of pieces of such image information.
There are frequent cases where a 3D volume image reconstructed from the raw data of a medical image of a region of interest (ROI) is used to acquire intuitive, effective information in order to diagnose the ROI.
In order to acquire the final version of a 3D image, the process of optimizing mesh information regarding the surface of the 3D image is gone through. In this case, the process of determining whether the mesh information of the 3D image is satisfactory through projection onto a two-dimensional (2D) version (2D plane) of the medical image and the performance of comparison and confirming or reediting the mesh information of the 3D image based on the result of the comparison is iteratively performed.
U.S. Pat. No. 8,942,455 registered on Jan. 27, 2015 and entitled “2D/3D Image Registration Method” discloses the process of optimizing the mesh information of a 3D image of an object, which is a prior art using registration between 2D and 3D images of an object.
Referring to FIG. 1, in the prior art, a 3D volume image of an anatomical ROI (for example, a heart) is acquired at step 102, and segmentation is automatically or manually performed at step 104. 3D mesh data is acquired through segmentation at step 106. At this point, the 3D mesh data acquired as described above has not yet gone through the verification of whether the data includes clinically useful information. FIG. 1 shows a case where 3D mesh information is used as a basis for registering a fluoroscopic image 108 that is acquired in real time. In other words, only the 3D mesh data does not include sufficient clinical information, and is used as guide information for registering a real-time fluoroscopic image. In this case, a 3D mesh is projected onto a 2D plane and provided in the form of a 2D mask at steps 110a and 110b, the pose of the 3D mesh is updated during the process of registering the 2D mask and the 2D fluoroscopic image with each other at step 112, and the 2D mask is optimized at step 116.
In other words, after the 3D mesh data has been projected onto the 2D plane, it is compared with an image having clinical information. Accordingly, the step of optimizing the 3D mesh data or 2D mask is a time-consuming step that is achieved only when the mask generation step 110a and 110b and the pose update step 112 are iteratively performed. The mask generation step 110a and 110b, the pose update step 112, and the optimization step 116 are each performed in independent display and computing environments. Therefore, a user must suffer from the inconvenience of work while iteratively reciprocating between different display and computing environments until the result of the mask generation step 110a and 110b is optimized at step 116.
In other words, as can be seen from the prior art, the step of optimizing 3D mesh data to include clinically useful information consumes a long period of time. Accordingly, prior arts are configured to iteratively perform the process of projecting a 3D volume and 3D mesh data onto a 2D plane, performing comparison, reconstructing the 3D mesh data, and performing optimization in order to overcome the above-described problem, and thus the problem in which the process is counterintuitive and inefficient remains still.